As the decommissioning and decontamination (D&D) of nuclear power plants (NPPs) has actively proceeded worldwide, the management of radiation exposure of workers has become more critical. Radioactive aerosol is one of the main causes of worker exposure, contributing to internal exposure by inhalation. It occurs in the process of cutting radioactive metal structures or melting radioactive wastes during D&D, and its distribution varies according to decommissioning strategies and cutting methods. Among the dominant radionuclides in radioactive aerosols, Fe-55 is known to be the most abundant. Fe-55, which decays by electron capture, is classified as a difficult-to-measure (DTM) radionuclide because its emitted X-rays have too low energy to measure directly from outside of the container. Generally, for measuring DTM nuclides, the liquid scintillation counting (LSC) method and the scaling factor (SF) method are used. However, these methods are not suitable for continuous monitoring of the D&D workplace due to the necessity of sampling and additional analysis. The radiation measurement system that can directly measure the radionuclides collected at the aerosol filter could be more useful. In this study, as preliminary research on developing the radioactive aerosol monitoring system, we fabricated a gamma-ray spectrometer based on a NaI (Tl) scintillator and measured the energy spectrum of Fe-55. A beryllium window was applied to the scintillator for X-ray transmission, and the Fe-55 check source was directly attached to the scintillator assuming that the aerosol filter was equipped. 5.9 keV photopeak was clearly observed and the energy resolution was estimated as 44.10%. Also, the simultaneous measurement with Cs-137 was carried out and all the peaks were measured.

Plastic scintillators can be used to find radioactive sources for portal monitoring due to their advantages such as faster decay time, non-hygroscopicity, relatively low manufacturing cost, robustness, and easy processing. However, plastic scintillators have too low density and effective atomic number, and they are not appropriate to be used to identify radionuclides directly. In this study, we devise the radiation sensor using a plastic scintillator with holes filled with bismuth nanoparticles to make up for the limitations of plastic materials. We use MCNP (Monte Carlo N-particle) simulating program to confirm the performance of bismuth nanoparticles in the plastic scintillators. The photoelectric peak is found in the bismuth-loaded plastic scintillator by subtracting the energy spectrum from that of the standard plastic scintillator. The height and diameter of the simulated plastic scintillator are 3 and 5 cm, respectively, and it has 19 holes whose depth and diameter are 2.5 and 0.2 cm, respectively. As a gamma-ray source, Cs-137 which emits 662 keV energy is used. The clear energy peak is observed in the subtracted spectrum, the full width at half maximum (FWHM) and the energy resolution are calculated to evaluate the performance of the proposed radiation sensor. The FWHM of the peak and the energy resolution are 61.18 keV and 9.242% at 662 keV, respectively.

Gamma-ray spectroscopy, which is an appropriate method to identify and quantify radionuclides, is widely utilized in radiological leakage monitoring of nuclear facilities, assay of radioactive wastes, and decontamination evaluation of post-processing such as decommissioning and remediation. For example, in the post-processing, it is conducted to verify the radioactivity level of the site before and after the work and decide to recycle or dispose the generated waste. For an accurate evaluation of gamma-ray emitting radionuclides, the measurement should be carried out near the region of interest on site, or a sample analysis should be performed in the laboratory. However, the region is inaccessible due to the safety-critical nature of nuclear facilities, and excessive radiation exposure to workers could be caused. In addition, in the case of subjects that may be contaminated inside such as pipe structures generated during decommissioning, surveying is usually done over the outside of them only, so the effectiveness of the result is limited. Thus, there is a need to develop a radiation measurement system that can be available in narrow space and can sense remotely with excellent performance. A liquid light guide (LLG), unlike typical optical fiber, is a light guide which has a liquid core. It has superior light transmissivity than any optical fiber and can be manufactured with a larger diameter. Additionally, it can deliver light with much greater intensity with very low attenuation along the length because there is no packing fraction and it has very high radiation resistant characteristics. Especially, thanks to the good transmissivity in UV-VIS wavelength, the LLG can well transmit the scintillation light signals from scintillators that have relatively short emission wavelengths, such as LaBr3:Ce and CeBr3. In this study, we developed a radiation sensor system based on a LLG for remote gamma-ray spectroscopy. We fabricated a radiation sensor with LaBr3:Ce scintillator and LLG, and acquired energy spectra of Cs-137 and Co-60 remotely. Furthermore, the results of gamma-ray spectroscopy using different lengths of LLG were compared with those obtained without LLG. Energy resolutions were estimated as 7.67%, 4.90%, and 4.81% at 662, 1,173, and 1,332 keV, respectively for 1 m long LLG, which shows similar values of a general NaI(Tl) scintillator. With 3 m long LLG, the energy resolutions were 7.92%, 5.48%, and 5.07% for 662, 1,173, and 1,332 keV gamma-rays, respectively.

In this study, the positions of Cs-137 gamma ray source are estimated from the plastic scintillating fiber bundle sensor with length of 5 m, using machine learning data analysis. Seven strands of plastic scintillating fibers are bundled by black shrink tube and two photomultiplier tubes are used as a gamma ray sensing and light measuring devices, respectively. The dose rate of Cs-137 used in this study is 6 μSv·h−1. For the machine learning modeling, Keras framework in a Python environment is used. The algorithm chosen to construct machine learning model is regression with 15,000 number of nodes in each hidden layer. The pulse-shaped signals measured by photomultiplier tubes are saved as discrete digits and each pulse data consists of 1,024 number of them. Measurements are conducted separately to create machine learning data used in training and test processes. Measurement times were different for obtaining training and test data which were 1 minute and 5 seconds, respectively. It is because sufficient number of data are needed in case of training data, while the measurement time of test data implies the actual measuring time. The machine learning model is designated to estimate the source positions using the information about time difference of the pulses which are created simultaneously by the interaction of gamma ray and plastic scintillating fiber sensor. To evaluate whether the double-trained machine learning model shows enhancement in accuracy of source position estimation, the reference model is constructed using training data with one-time learning process. The double-trained machine learning model is designed to construct first model and create a second training data using the training error and predetermined coefficient. The second training data are used to construct a final model. Both reference model and double-trained models constructed with different coefficients are evaluated with test data. The evaluation result shows that the average values calculated for all measured position in each model are different from 7.21 to 1.44 cm. As a result, by constructing the double-trained machine learning model, the final accuracy shows 80% of improvement ratio. Further study will be conducted to evaluate whether the double-trained machine learning model is applicable to other data obtained from measurement of gamma ray sources with different energy and set a methodology to find optimal coefficient.

Methyl bromide (MB) has been still routinely used in quarantine fumigation on imported citrus, although there had been issuing chronic inhalation toxicity to fumigators and related workers as well as phytotoxic damages after fumigation. Ethyl formate (EF), is the only option to replace MB in terms of its safety for consumers (food additive and naturally occurred) and worker with higher threshold level limit (TLV = 100 ppm). Its application technology also provide cost effectiveness, good commercial practice in terms of application time (< 10 min) for 40 ft container. The replacement of MB with EF is recommended not to fumigate with hazardous and phasing-out MB on imported oranges.

Urokinas type plasminogen activator (uPA) has been used as a therapeutic agent for treating human diseases such as thrombosis. Attempts to transgenically overexpress the uPA in animal bioreactors have been hampered due to side effects associated with this functional protein hormone on homeostasis. Recently, chicken has been emerged as a potential candidate for use as bioreactor to produce proteins of pharmaceutical importance. Since this species has low homology uPA sequence with mammals, we hypothesized that chicken could be used as a potential bioreactor for production of human uPA. In this study, using replication‐defective Murine Leukemia Virus (MLV)‐based retrovirus vectors encapsidated with Vesicular Stomatitis Virus G Glycoprotein (VSV‐G), we attempted to make transgenic chicken expressing human uPA (huPA). The recombinant retrovirus was injected beneath the blastoderm of non‐incubated chicken embryos (stage X, at laying). After 21 days of incubation (at hatching), all of the 38 living chicks that assayed, were found to express the vector‐encoded huPA gene in various organs and tissues, which was under the control of the Rous Sarcoma Virus (RSV) or Cytomegalovirus (CMV) promoter. Using specific primer set for huPA, PCR and RTPCR analyses of gDNA isolated from these samples demonstrated these chickens were transgenic for huPA. Furthermore, successful germ line transmission of huPA transgene was confirmed and next generation whole body huPA transgenic chickens were also produced. We also assayed huPA protein titer in blood (17.1 IU/ml) and eggs (4.4 IU/ml) of whole body huPA transgenic chicken. Thus, our results demonstrated that chicken could be used as bioreactors to produce huPA.

In the present study, we have developed a high-frequency plant regeneration system for Italian ryegrass via callus culture using mature seeds as explants. Optimal embryogenic callus induction was found to occur in MS medium containing 5 ㎎ 1?¹ 2,4-D, 0.5 ㎎ 1?¹ BA, 500 ㎎ 1?¹ L-proline, 1 g 1?¹ casein hydrolysate, 30 g 1?¹ sucrose, 7 ㎎ 1?¹ AgNO₃, 2 ㎎ 1?¹ CuSO₄ and solidified with 3 g 1?¹ Gelrite. The highest regeneration rate was obtained in MS medium containing 1 ㎎ 1?¹ 2,4-D, 5 ㎎ 1?¹ BA, 500 ㎎ 1?¹ L-proline, 1 g 1?¹ casein hydrolysate, 1 ㎎ 1?¹ thiamine-HCl, 30 g 1?¹ sucrose, 7 ㎎ 1?¹ AgNO₃, 2 ㎎ 1?¹ CuSO₄ and solidified with 3 g 1?¹ Gelrite. By using the most effective treatment determined for each parameter, the highest rates of embryogenic callus formation (48.9%) and regeneration (47.6%) were obtained with the Hwasan 101 cultivar. The overall plant regeneration rates of the examined cultivars ranged from 7.5% to 23.2%. Thus, optimization of regeneration frequency using mature seeds as explant material may offer a simple and efficient protocol for Italian ryegrass that may improve molecular breeding of this species.

대형 건축물의 진동과 같은 초저주파 진동특성의 해석을 수행하기 위해서는 측정대상 및 측정조건, 그리고 목적으로 하는 측정요소에 적합한 측정시스템이 구축되어야 하며, 구축된 측정시스템으로부터 얻어진 극히 제한된 유한량의 데이터로부터 목적하는 특성요소를 추출하기 위한 정밀한 데이터분석기술이 요구된다. 따라서, 본 연구에서는 고신뢰성을 저주파진동 특성의 분석을 위한 효과적인 데이터처리기법의 개발을 목표로, 측정조건에 따른 저주파진동 해석의 문제점을 분석하고, FFT법과 MEM법의 저주파응답 특성을 비교하였으며, 비교결과를 토대로 저주파진동 해석에 적합한 알고리즘을 결정하였다. 또한, 결정된 분석 알고리즘의 성능을 명확히 하여 정밀분석을 위한 측정데이터의 최적조건을 제시하였다.

본 연구에서는 :1[조처l 의 우l 상학적 최서화플 위한 비선형 formulation (NLP )가 개발， 검토되었다. 이 NLP 는 multiple-loading 하에샤 염악의 ~Ç> __ ti.젝E] _tl_ 함수， 응력， 맨위 제약조간단블 쉽게 다붙 수가 있다. 또한 이 NLP 는 해삭파 최석화 디자인블 농사에 실시함으보써 요소 사이즈가 영 P 료 집까함에 따른 강성 매트린스의 singularity플 피한 수 있다. 즉， 평형 방정삭블 등제약조끼으로 치흰함으깊써 강성 1깨트릭스 그자체나 그의 역매트박스륜 구한 펀요도 없어진다 이 NLP 든 multiple-loading condition 하에서 테 스트 되었으며， 이플 풍 해 이 NLP 가 다양한 세약조건하에서 강략하셰 작용함이 입증되었다-

구조물에 있어서 위상학적 최적화 문제는 최적화를 구하는 과정에서 구조체가 변화함으로 인한 어려웅 때 문에 최적화 분야에서 가장 어려운 문제로 간주되어 왔다. 종래의 방법으로는 일반적으로 구조 요소 사이즈가 영으로 접근할 때 강성 매트릭스의 singularity를 발생시킴으로써 최적의 혜를 얻지 못하고 도중에 계산이 종 료되어 버린다. 본 연구에 있어서는 이러한 문제점들을 해결하기 위한 비선형 프로그래밍 formulation을 제 안하는 것올 목적으로한다. 이 formulation 의 주된 특성은 요소 사이즈가 영이 되는 것을 허용한다. 평형 방정 식 을 둥제 약조건으로 간주함으로써 강성 매 트릭 스의 singularity를 피 할 수 있다. 이 formulation을 하중올 받는 구조물에 있어서 웅력과 변위의 제약조 건 하에서 중량을 최소화할때의 유한 요소의 두께를 구하는 디자 인 문제에 적용하여， 이 formulation 이 위상화적 최적화에 있어서의 효과를 입증하였다.

본 연구의 목적은부착되어 있거나혹은부착되어 있지 않는텐돈을 갖고 있는프리스트래스트콘크리트구 조체의 해석이 가능한 프로그랩올 개발하는데 있다. 이를 위해서 먼저 콘크리트， 철근， 그리고 텐돈둥의 반복 하중에 대한모델을선정 개발하고， 텐돈이 부착되어 있지 않는경우에 그웅력 및 변형도를보다정확하게 해 석할 수 있는 복합유한요소법을 유도하였다. 이러한 복합 유한요소법을 사용하였올때 요소의 변형 형상을 가 정하지 않고도 각 단면들의 변형을 결정할 수 있어 요소의 길이가 기존의 유한요소법에 비하여 상당히 길어질 수 있다. 이러한 복합유한요소볍올 가능하 게 하기 위해서 다양한 형태의 적분 방법이 프로그랩되었다. 그리고 텐돈이 부착되 어 있지 않는 경우 그효과를 예측할 수 있는 방법이 개 발되 었다. 끝으로， 실제 구조체에 대한 해 석결과 및 실제실험에 대한 해석결과를 비교하여 프로그랭의 적용성올 검토하였다.

본 연구에서는 철근콘크리트 기둥에서 주근의 좌굴성장을 수치 해석법으로 조사하는 것을 목적으로 콘크리트의 모델과 철근과 콘크리트의 경계면에서의 상호 힘의 전달을 나타내는 수치 해석모델을 제안하였다. 본 연구에서 제안된 수치 해석법에 의해 콘크리트의 변형도 연화 및 철근의 좌굴을 고려한 해석이 가능한 것으로 판단되었으며 또한 취급된 해석예에서는 주근의 좌굴은 띠철근의 간격 및 주근의 배근위치에 크게 영향을 받는다는 것을 알 수 있다.